Applications with little or no rebound Digitalization and the rebound effect

(1)

Applications with little or no

rebound

Digitalization and the

rebound effect

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Full rebound

2

A thought experiment

Budget: 100 CHF

Goal: Drive as far as possible.

Fuel price: 1 CHF/L Scenario 0

Efﬁciency: 1 km/L

Distance: 100 km Fuel used: 100 L

Fuel price: 1 CHF/L Efﬁcient cars

Efﬁciency: 2 km/L

Distance: 200 km Fuel used: 100 L

Fuel price: 2 CHF/L Increased fuel price

Efﬁciency: 2 km/L

Distance: 100 km

Fuel used: 50 L

No rebound

(3)

Application 1: Japanese

vending machines

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Japanese vending machines (VM)

● 5.4 Millions machines (1990). → One machine per 23 Japanese.

● 3.7% of total electricity consumed in Japan (Coleman 1997).

● Biggest contribution from refrigerated VMs that are running 24/7.

● Energy efﬁciency improved by 58% within 15 years (since 1990).

Image source: https://commons.wikimedia.org/wiki/File:Vending_machine_of_soft_drink_and_ice_cream_in_Japan.jpg 4

[1][6]

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Where is the rebound?

Hilty’s hypothesis:

● Old machines required a lot of consumers to be proﬁtable.

→ Only installed in large ofﬁces.

● Efﬁcient machines open up the marked:

Proﬁtable to install in smaller ofﬁces.

Data for EU, but it is roughly the same in all economies.

Proﬁtable locations so far.

Proﬁtable locations with new efﬁcient VMs.

[1]

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Linear decrease of potential

customers per machine generates exponential increase of marketable VMs.

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[1]

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Worse than rebound...

A1

A2

A1 < A2

⇒ Improved energy efﬁciency resulted in increased overall energy consumption.

Backﬁre

[1]

But this was just

speculation...

(8)

How it looks in reality:

8

Energy consumption per machine.

#VMs in Tokyo.

Where is the expected rebound/backﬁre?

[4]

(9)

Expectation Reality

?

Underlying assumptions:

1. If money can be made, money will be made.

2. Increased efﬁciency ⇒ Reduced operational cost.

3. Growth of VM market is only limited

[1][4]

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Limiting factors

Two additional factors limiting the growth of japanese VMs:

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Space Fukushima nuclear disaster (2011)

Area south of meguro station.

(Image source: https://sagasoda.com)

Aerial view of the fukushima catastrophe/

(Image source: https://peaceandjustice.org.uk)

[1]

(11)

Application 2: Multi-site

conferences

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General idea

12

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General idea

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General idea

14

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System under study: 2-site event

● Between University of Zurich and Nagoya University.

● 531 participants (372 in Davos, 159 in Nagoya).

● Partially shared events due to time-difference.

Davos, CH Nagoya,

JPN

SHARED

JPN CH

8 AM 12 AM

3 PM 7 PM

CH time:

JPN time:

[2]

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ICT infrastructure

16

TelePresence system by Cisco.

[2]

Fotos graciously provided by Vlad Coroamă

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Social interactions during breaks

Attendees were generally satisﬁed with the cross-site interactions.

(Q&A, coffee breaks etc.)

[2]

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Measuring environmental impact (EI)

● Investigate difference in CO2 emissions between 1-site and multi-site model.

● Non travel-related EI assumed to be the same in both models.

(I.E. Program booklet, ﬂyers, hotel stay etc.)

● 96.3% of EI in 1-site conferences due to travel of attendees (Hischier and Hilty).

⇒ Focus only on rebound effects w.r.t. travel.

● Impact of additional ICT infrastructure needed for 2-site event was shown to be negligible (even under most pessimistic assumptions).

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[2][7]

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Assessing the travel emissions

● Estimates from ecoinvent database.

● Attendees speciﬁed speciﬁed the different travel stops, and used means of transportation.

0.17kg

0.11kg

0.24kg

Example:

1. Airplane: New York → Paris 2. Airplane: Paris → Zurich;

3. Train: Zurich Airport → Davos

[2]

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Alternative scenarios

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AS-IS

Nagoya-only Davos-only

● Hypothetical scenarios obtained from survey + extrapolation:

“Would you travel to Nagoya if the conference was only in Japan?”

● Hypothetical travel routes were calculated under very conservative assumptions: Only train + direct long-haul ﬂight.

[2]

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Results: travel emissions

CO2 (#Attendees) Davos Nagoya Total

Nagoya-only 154 t ⁽⁷⁹⁾ 35 t ⁽¹⁵⁹⁾ 189 t ⁽²³⁸⁾ Davos-only 84 t (372) 151 t (76) 235 t (448)

AS-IS 84 t (372) 35 t (159) 119 t (531)

[2]

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Substitution: Davos-only ⇒ Davos-Nagoya

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Attendees Davos-only Davos-Nagoya

Davos 372 372

Nagoya 76 159

76 + 83

● 76 who would have travelled to Davos, will still travel to Davos.

● 83 additional Japanese will attend if there is the possibility to attend in Nagoya.

→ This is the expected rebound: 2-site attracts more people than 1-site.

● Still: Overall reduction of CO2 by almost 50%: 235 t → 119 t. (last slide)

● Similar results in the other substitution (Nagoya-only → Davos-Nagoya).

[2]

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Application 3: Real-time

feedback during showers

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The salience

bias “The salience bias describes our

tendency to focus on items or information that are more

noteworthy while ignoring those that do not grab our attention.”

Image source: https://thedecisionlab.com/biases/salience-bias/ Quote source: https://thedecisionlab.com/biases/salience-bias/ 24

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Salience bias example: Showering

● Immediate “reward” of sensual comfort during warm showers overshadow the negative EI of water and energy consumption.

● Idea: Make energy consumption more salient by real-time feedback.

● Many people indicate willingness change behaviour in order to protect environment. (Diekmann et al.

2009, Naderi 2011)

[3][10][11]

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Environmental impact of showers

● On average: 45 L of hot water per 5 minutes of showering.

→ Requires 2.6 kWh to heat up.

● Water heating is second biggest contributing factor to residential energy usage.

● 14%-18% of average home’s energy use.

(Swiss Federal Ofﬁce for the Environment 2013)

26 2.6 kWh

1.0 kWh

0.63 kWh

[3][8][9]

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The device

● Activated automatically each shower.

→ Unit of measurement = 1 shower.

● Measures energy and water consumption.

● Also displays water temperature and duration of shower.

● Polar bear: ice ﬂoe melts as energy consumption increases.

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Experimental conditions

● Duration of study: 2 month.

● Roughly 700 participating households.

● Only 1- and 2-person households were admitted.

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Real-time condition

Control

Real-time + past feedback

● Water temperature [°C]

● Water consumption [L]

● Energy consumption [kWh]

● Water temperature [°C] ● Water temperature [°C]

● Water consumption [L]

● Energy consumption [kWh]

● Water consumption of previous shower [L]

[1][4]

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Results

[3]

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Results

30

[3]

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Results

22% drop of energy use.

[3]

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Conclusion

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Comparison of the applications

1: Japanese VM 2: Multi-site conferences 3: Real-time feedback

Rebound effect dramatically reduced by market

saturation.

⇒ Energy improvements translate directly to reduced energy consumption.

Rebound might not be as bad if the efﬁciency gain is sufﬁciently big.

Consumer “becomes more efﬁcient” (i.e. uses less energy to shower).

⇒ Directly translates to energy savings.

[1][2][3]

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The car example (cont.)

Budget: 100 CHF

Goal: Drive as far as possible.

34

Fuel price: 1 CHF/L Scenario 0

Efﬁciency: 1 km/L

Distance: 100 km Fuel used: 100 L

Fuel price: 1 CHF/L Large efﬁciency increase

Efﬁciency: 4 km/L

Fuel price: 1 CHF/L Becoming CO2 aware

Efﬁciency: 1 km/L

Distance: 50 km Fuel used: 50 L Japanese VM Multi-site conferences Real-time feedback

Fuel price: 2 CHF/L Limited Market

Efﬁciency: 2 km/L

50 chf

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Closing thoughts

(36)

The rebound dilemma (from an economic perspective)

36

High energy cost?

Big economic incentive to be more energy

efﬁcient...

… in order to increase production. (Rebound!)

Improved energy

efﬁciency unlikely to cause rebound effects...

… but also no economic incentive.

YES NO

(Hilty, 2012) Why energy efﬁciency is not sufﬁcient – some remarks on "Green by IT"

[1]

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Energy sufficiency

“Energy sufficiency goes beyond energy efficiency: it’s about having enough but not using too much. It’s about doing things differently; about living well, within the limits.”

● Cycle to a nearby destination instead of using car.

● Reduce thermostat by 1 degree.

● Shower less.

Enforcing policy Enabling policy Implicit policy

“No cars allowed.” Provide public infrastructure.

(e.g. bikelanes) Social pressure.

[5]

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Downshifting

38

shifting

Source: Paramount pictures

[5]

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Higher income ⇒ Higher GHG emissions

Downshifting

[5][11]

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Thank you :-)

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References (1 / 3)

[1] Lorenz M. Hilty. Why energy efﬁciency is not sufﬁcient – some remarks on "Green by IT", Proceedings of the 26th Environmental Informatics Conference (EnviroInfo), pp. 13-20, 2012

[2] Vlad C. Coroamă, Lorenz M. Hilty and Martin Birtel. Effects of Internet-based multiple-site conferences on greenhouse gas emissions, Telematics & Informatics, 29 (4), pp. 362-374, 2012

[3] Verena Tiefenbeck, Lorenz Goette, Kathrin Degen, Vojkan Tasic, Elgar Fleisch, Rafael Lalive and Thorsten Staake. Overcoming Salience Bias: How Real-Time Feedback Fosters Resource Conservation, Management Science, 64 (3), pp. 1458-1476, 2018

[4] Masahito Takahashi and Hiroshi Asano. Japanese Vending Machine and Display Cooler Energy Use Affected by Principal-Agent Problem, In: Quantifying the Effects of Market Failures in the End-Use of Energy, pp. 108–119, International Energy Agency, 2006

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References (2 / 3)

[5] Steve Sorrell, Birgitta Gatersleben and Angela Druckman. Energy sufﬁciency and rebound effects (Concept paper), 2018

[6] Coleman, J. Japan’s vending machines not as upright as they look, The Sunday Gazette, 1997

[7] Hischier R and Hilty L. Environmental impacts of an international conference. Environmental Impact Assessment Review 22, 543–557, 2002

[8] Lapillonne B, Pollier K and Samci N. Energy efﬁciency trends for households in the EU. Report, ODYSSEE-MURE, ADEME, 2015

[9] Michel A, Attali S, and Bush E. Energy efﬁciency of white goods in Europe: Monitoring the market with sales data. Technical report, Topten International Services, Zürich, 2015

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References (3 / 3)

[10] Diekmann A, Meyer R, Mühlemann C and Diem A. Schweizer Umweltsurvey 2007—Analysen und Ergebnisse (Swiss environmental survey—Analyses and results). Report to the Swiss Federal Statistical Ofﬁce (BFS) and to the Federal Ofﬁce for the Environment (BAFU). Technical report, ETH Zurich, 2009 [11] Naderi I. Green behavior: Concern for the self or others? AMA Summer Educators’ Conf. Proc., Vol. 22 (American Marketing Association, Chicago), 163–164., 2011

[12] Chitnis M, Sorrell S, Druckman A. Firth, S.K.; Jackson, T., Turning lights into ﬂights: Estimating direct and indirect rebound effects for UK households. Energy Policy, 55, 234-250, 2013